24UL - Mangrove Kingfisher

Wind trajectory

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library(GeoPressureR)
library(tidyverse)
library(leaflet)
library(leaflet.extras)
library(raster)
library(dplyr)
library(ggplot2)
library(plotly)
knitr::opts_chunk$set(echo = FALSE)
load(paste0("../data/1_pressure/", params$gdl_id, "_pressure_prob.Rdata"))
load(paste0("../data/2_light/", params$gdl_id, "_light_prob.Rdata"))
load(paste0("../data/3_static/", params$gdl_id, "_static_prob.Rdata"))
load(paste0("../data/5_wind_graph/", params$gdl_id, "_wind_graph.Rdata"))
load(paste0("../data/5_wind_graph/", params$gdl_id, "_grl.Rdata"))
col <- rep(RColorBrewer::brewer.pal(8, "Dark2"), times = ceiling(max(pam$sta$sta_id) / 8 + 1))

Altitude

Altitudes are computed based on pressure measurement of the geolocation, corrected based on the assumed location of the shortest path. This correction accounts therefore for the natural variation of pressure as estimated by ERA-5. The vertical lines indicate the sunrise (dashed) and sunset (solid).

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p <- ggplot() +
  geom_vline(data = twl, aes(xintercept = twilight, linetype = ifelse(rise, "dashed", "solid")), lwd=0.1, color="grey") +
  geom_line(data = do.call("rbind", shortest_path_timeserie), aes(x = date, y = altitude)) +
  geom_line(data = do.call("rbind", shortest_path_timeserie) %>% filter(sta_id > 0), aes(x = date, y = altitude, col = factor(sta_id))) +
  theme_bw() +
  scale_colour_manual(values = col) +
  scale_y_continuous(name = "Altitude (m)")

ggplotly(p, dynamicTicks = T) %>% layout(showlegend = F)

Wintering location

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file <- paste0("figure_print/wintering_location/wintering_location_",params$gdl_id,".png")
if(file.exists(file)){
  knitr::include_graphics(file)
}

Latitude time

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 tmp <- lapply(pressure_prob, function(x) {
    mt <- metadata(x)
    df <- data.frame(
      start = mt$temporal_extent[1],
      end = mt$temporal_extent[2],
      sta_id = mt$sta_id
    )
  })
  tmp2 <- do.call("rbind", tmp)

sim_lat <- as.data.frame(t(path_sim$lat)) %>%
  mutate(sta_id = path_sim$sta_id) %>%
  pivot_longer(-c(sta_id)) %>%
  left_join(tmp2,by="sta_id")

sim_lat_p <- sim_lat %>%
  filter(sta_id==max(sta_id)) %>%
  mutate(start=end) %>%
  rbind(sim_lat)

sp_lat <- as.data.frame(shortest_path) %>% left_join(tmp2,by="sta_id")

sp_lat_p <- sp_lat %>%
  filter(sta_id==max(sta_id)) %>%
  mutate(start=end) %>%
  rbind(sp_lat)

p <- ggplot() +
  geom_step(data=sim_lat_p, aes(x=start, y=value, group=name), alpha=.07) +
  geom_point(data=sp_lat_p, aes(x=start, y=lat)) +
  xlab('Date') +
  ylab('Latitude') +
  theme_light()

ggplotly(p, dynamicTicks = T)

Shortest path and simulated path

The large circles indicates the shortest path (overall most likely trajectory) estimated by the graph approach. The size is proportional to the duration of stay. The small dots and grey lines represents 10 possible trajeectories of the bird according to the model.

Click on the full-screen mode button on the top-left of the map to see more details on the map.

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sta_duration <- unlist(lapply(static_prob_marginal, function(x) {
  as.numeric(difftime(metadata(x)$temporal_extent[2], metadata(x)$temporal_extent[1], units = "days"))
}))
pal <- colorFactor(col, as.factor(seq_len(length(col))))
m <- leaflet(width = "100%") %>%
  addProviderTiles(providers$Stamen.TerrainBackground) %>%
  addFullscreenControl() %>%
  addPolylines(lng = shortest_path$lon, lat = shortest_path$lat, opacity = 1, color = "#808080", weight = 3) %>%
  addCircles(lng = shortest_path$lon, lat = shortest_path$lat, opacity = 1, color = pal(factor(shortest_path$sta_id, levels = pam$sta$sta_id)), weight = sta_duration^(0.3) * 10)

for (i in seq_len(nrow(path_sim$lon))) {
  m <- m %>%
    addPolylines(lng = path_sim$lon[i, ], lat = path_sim$lat[i, ], opacity = 0.5, weight = 1, color = "#808080") %>%
    addCircles(lng = path_sim$lon[i, ], lat = path_sim$lat[i, ], opacity = .7, weight = 1, color = pal(factor(shortest_path$sta_id, levels = pam$sta$sta_id)))
}
m

Marginal probability map

The marginal probability map estimate the overall probability of position at each stationary period regardless of the trajectory taken by the bird. It is the most useful quantification of the uncertainty of the position of the bird.

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li_s <- list()
l <- leaflet(width = "100%") %>%
  addProviderTiles(providers$Stamen.TerrainBackground) %>%
  addFullscreenControl()
for (i_r in seq_len(length(static_prob_marginal))) {
  i_s <- metadata(static_prob_marginal[[i_r]])$sta_id
  info <- metadata(static_prob_marginal[[i_r]])$temporal_extent
  info_str <- paste0(i_s, " | ", info[1], "->", info[2])
  li_s <- append(li_s, info_str)
  l <- l %>%
    addRasterImage(static_prob_marginal[[i_r]], colors = "OrRd", opacity = 0.8, group = info_str) %>%
    addCircles(lng = shortest_path$lon[i_s], lat = shortest_path$lat[i_s], opacity = 1, color = "#000", weight = 10, group = info_str)
}
l %>%
  addLayersControl(
    overlayGroups = li_s,
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  hideGroup(tail(li_s, length(li_s) - 1))

Wind assistance

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  fun_marker_color <- function(norm){
    if (norm < 20){
      "darkpurple"
    } else if (norm < 35){
      "darkblue"
    } else if (norm < 50){
      "lightblue"
    } else if (norm < 60){
      "lightgreen"
    } else if (norm < 80){
      "yellow"
    } else if (norm < 100){
      "lightred"
    } else {
      "darkred"
    }
  }
  fun_NSEW <- function(angle){
    angle <- angle  %% (pi* 2)
    angle <- angle*180/pi
    if (angle < 45/2){
      "E"
    } else if (angle < 45*3/2){
      "NE"
    } else if (angle < 45*5/2){
      "N"
    } else if (angle < 45*7/2){
      "NW"
    } else if (angle < 45*9/2){
      "W"
    } else if (angle < 45*11/2){
      "SW"
    } else if (angle < 45*13/2){
      "S"
    }else if (angle < 45*15/2){
      "SE"
    } else {
      "E"
    }
  }

  sta_duration <- unlist(lapply(static_prob_marginal,function(x){as.numeric(difftime(metadata(x)$temporal_extent[2],metadata(x)$temporal_extent[1],units="days"))}))

  m <-leaflet(width = "100%") %>%
    addProviderTiles(providers$Stamen.TerrainBackground) %>%  addFullscreenControl() %>%
    addPolylines(lng = shortest_path$lon, lat = shortest_path$lat, opacity = 1, color = "#808080", weight = 3) %>%
    addCircles(lng = shortest_path$lon, lat = shortest_path$lat, opacity = 1, color = "#000", weight = sta_duration^(0.3)*10)

  for (i_s in seq_len(grl$sz[3]-1)){
    if (grl$flight_duration[i_s]>5){
      edge <- which(grl$s == shortest_path$id[i_s] & grl$t == shortest_path$id[i_s+1])

      label = paste0( i_s,': ', grl$flight[[i_s]]$start, " - ", grl$flight[[i_s]]$end, "<br>",
                      "F. dur.: ", round(grl$flight_duration[i_s]), ' h <br>',
                      "GS: ", round(abs(grl$gs[edge])), ' km/h, ',fun_NSEW(Arg(grl$gs[edge])),'<br>',
                      "WS: ", round(abs(grl$ws[edge])), ' km/h, ',fun_NSEW(Arg(grl$ws[edge])),'<br>',
                      "AS: ", round(abs(grl$as[edge])), ' km/h, ',fun_NSEW(Arg(grl$as[edge])),'<br>')

      iconArrow <- makeAwesomeIcon(icon = "arrow-up",
                                   library = "fa",
                                   iconColor = "#FFF",
                                   iconRotate = (90 - Arg(grl$ws[edge])/pi*180) %% 360,
                                   squareMarker = TRUE,
                                   markerColor = fun_marker_color(abs(grl$ws[edge])))

      m <- m %>% addAwesomeMarkers(lng = (shortest_path$lon[i_s] + shortest_path$lon[i_s+1])/2,
                                   lat = (shortest_path$lat[i_s] + shortest_path$lat[i_s+1])/2,
                                   icon = iconArrow, popup = label)
    }
  }
  m

Histogram of Speed

Show code

edge <- t(graph_path2edge(path_sim$id, grl))
nj <- ncol(edge)
nsta <- ncol(path_sim$lon)

speed_df <- data.frame(
  as = abs(grl$as[edge]),
  gs = abs(grl$gs[edge]),
  ws = abs(grl$ws[edge]),
  sta_id_s = rep(head(grl$sta_id,-1), nj),
  sta_id_t = rep(tail(grl$sta_id,-1), nj),
  flight_duration = rep(head(grl$flight_duration,-1), nj),
  dist = geosphere::distGeo(
    cbind(as.vector(t(path_sim$lon[,1:nsta-1])), as.vector(t(path_sim$lat[,1:nsta-1]))),
    cbind(as.vector(t(path_sim$lon[,2:nsta])),   as.vector(t(path_sim$lat[,2:nsta])))
  ) / 1000
) %>% mutate(
  name = paste(sta_id_s,sta_id_t, sep="-")
)

plot1 <- ggplot(speed_df, aes(reorder(name, sta_id_s), gs)) + geom_boxplot() + theme_bw() +scale_x_discrete(name = "")
plot2 <- ggplot(speed_df, aes(reorder(name, sta_id_s), ws)) + geom_boxplot() + theme_bw() +scale_x_discrete(name = "")
plot3 <- ggplot(speed_df, aes(reorder(name, sta_id_s), as)) + geom_boxplot() + theme_bw() +scale_x_discrete(name = "")
plot4 <- ggplot(speed_df, aes(reorder(name, sta_id_s), flight_duration)) + geom_point() + theme_bw() +scale_x_discrete(name = "")

subplot(ggplotly(plot1), ggplotly(plot2), ggplotly(plot3), ggplotly(plot4), nrows=4, titleY=T)

Table of transition

Show code

alt_df = do.call("rbind", shortest_path_timeserie) %>%
    arrange(date) %>%
    mutate(
      sta_id_s = cummax(sta_id),
      sta_id_t = sta_id_s+1
    ) %>%
    filter(sta_id == 0 & sta_id_s > 0 ) %>%
    group_by(sta_id_s, sta_id_t) %>%
    summarise(
      alt_min = min(altitude),
      alt_max = max(altitude),
      alt_mean = mean(altitude),
      alt_med = median(altitude),
    )

  trans_df <- speed_df  %>%
    group_by(sta_id_s,sta_id_t,flight_duration) %>%
    summarise(
      as_m = mean(as),
      as_s = sd(as),
      gs_m = mean(gs),
      gs_s = sd(gs),
      ws_m = mean(ws),
      ws_s = sd(ws),
      dist_m = mean(dist),
      dist_s = sd(dist)
    ) %>%
    left_join(alt_df)

trans_df %>% kable()

sta_id_s	sta_id_t	flight_duration	as_m	as_s	gs_m	gs_s	ws_m	ws_s	dist_m	dist_s	alt_min	alt_max	alt_mean	alt_med
1	2	1.000000	40.71811	19.31219	41.36230	18.49849	12.74189	0.3309901	41.36230	18.49849	-24.657849	200.5658	111.2848	112.3381
2	3	6.333333	42.50371	20.08416	43.96397	22.36945	10.71927	1.8907470	278.43845	141.67317	165.016471	1499.0057	715.7429	492.2210
3	4	4.500000	43.90570	18.01905	41.71438	17.53903	13.48362	3.1948301	187.71473	78.92562	127.680428	457.2872	229.1131	198.5260
4	5	9.000000	44.86375	23.71090	41.82512	27.04712	10.28025	0.7241998	376.42612	243.42408	129.453340	1321.3593	582.2717	393.2343
5	6	8.000000	36.63322	17.31557	49.11755	19.28926	13.31011	3.9187046	392.94039	154.31407	145.927400	575.0286	301.3133	270.7896
6	7	5.000000	42.54543	21.08830	58.23538	31.02983	23.33457	1.8005657	291.17688	155.14914	116.395485	463.0372	309.0314	319.0316
7	8	2.166667	34.59024	10.94089	37.17547	17.36652	20.81042	0.4649910	80.54685	37.62745	5.517547	356.5372	221.7808	231.5882